Head-to-Head Model Comparison

Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Qwen3.5 397B-A17BProfile

Qwen3.5 122B-A10BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3.5 397B-A17B

Modello Base a Pesi Aperti

Qwen3.5 397B-A17B di Alibaba è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 262K, velocità di 64 token/s, indice di intelligenza di 86/1

Intelligence Index86 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

58%

Alibaba

Qwen3.5 122B-A10B

IA Multimodale Nativa

Qwen3.5 122B-A10B di Alibaba è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 262K, velocità di 78 token/s, indice di intelligenza di 82/1

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Qwen3.5 122B-A10B is faster at 78 tok/s vs 64 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Qwen3.5 397B-A17B	Qwen3.5 122B-A10B
Intelligence Index Composite reasoning & knowledge score (0-100)	86	82
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	16K	16K
Throughput Speed Tokens generated per second	64 tok/s	78 tok/s
SWE-bench Verified Real-world software engineering	58%	N/A
GPQA Diamond Graduate-level reasoning	70%	65.5%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3.5 122B-A10B

+22% faster generation

Context

Tied

262.1K tokens window

Punteggi Benchmark Verificati

Official evaluation metrics verified by independent third-party research labs.

Qwen3.5 397B-A17BQwen3.5 122B-A10B

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

58%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

70%vs65.5%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

83.5%vs80.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1385ptsvs1350pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

64 tok/s vs78 tok/s

Latenza e Tempo di Risposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Qwen3.5 397B-A17B0.65s

Qwen3.5 122B-A10B0.51s

Est. End-to-End Response (500 tokens)TTFT + generation time

Qwen3.5 397B-A17B8.46s

Qwen3.5 122B-A10B6.92s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Qwen3.5 397B-A17B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.5 122B-A10B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Qwen3.5 397B-A17B · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 16K token (SWE-bench Verified 58% · GPQA Diamond 70%)
Throughput elevato di 64 token/secondo con latenza al primo token (TTFT) di soli 650 ms
Pesi aperti (397B total (17B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Qwen3.5 122B-A10B · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 16K token (GPQA Diamond 65.5% · MMLU-Pro 80.5%)
Throughput elevato di 78 token/secondo con latenza al primo token (TTFT) di soli 510 ms
Pesi aperti (122B total (10B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Qwen3.5 397B-A17B	Qwen3.5 122B-A10B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	22K	18K
Reasoning Tokens / Task Thinking tokens burned per task	8K	5K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B: Risposte Rapide (FAQ)

Which is more intelligent, Qwen3.5 397B-A17B or Qwen3.5 122B-A10B?

Qwen3.5 397B-A17B is more intelligent. It scores 86 compared with 82 on the composite Intelligence Index.

Which model is faster?

Qwen3.5 122B-A10B is faster, generating 78 tokens per second compared with 64 tokens per second.

Which model has lower latency?

Qwen3.5 122B-A10B has lower latency, with a time to first token of 0.51s compared with 0.65s.

Verdetto

Sia Qwen3.5 397B-A17B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Qwen3.5 122B-A10B (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Qwen3.5 397B-A17B guida nei benchmark di ragionamento e programmazione, Qwen3.5 122B-A10B garantisce una generazione più rapida e Qwen3.5 397B-A17B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Qwen3.5 397B-A17B o Qwen3.5 122B-A10B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Qwen3.5 397B-A17B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Qwen3.5 397B-A17B e Qwen3.5 122B-A10B?+

Qwen3.5 397B-A17B costa $0.00 / $0.00 per milione di token di input/output, mentre Qwen3.5 122B-A10B costa $0.00 / $0.00. Nel costo combinato, Qwen3.5 397B-A17B risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Qwen3.5 397B-A17B supporta 262.1K token di contesto contro 262.1K token di Qwen3.5 122B-A10B, mentre Qwen3.5 122B-A10B offre una velocità di generazione superiore.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Feature	Qwen3.5 397B-A17B	Qwen3.5 122B-A10B
Official API Model ID (SDK)	qwen3-5-397b-a17b-instruct	qwen3-5-122b-a10b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2026-02
Knowledge Cutoff	December 2025	December 2025
Architecture	Next-Gen Multimodal Sparse Mixture-of-Experts	Efficient Open Multimodal MoE Transformer
Parameters	397B total (17B active MoE)	122B total (10B active MoE)
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 246 GB VRAM (4-bit Quantized) · 854 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 262 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text, vision
Output Modalities	text	text

Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B

Cambia e Confronta Qualsiasi Modello

Qwen3.5 397B-A17B

Qwen3.5 122B-A10B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Qwen3.5 397B-A17B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.5 122B-A10B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Qwen3.5 397B-A17B vs Qwen3.5 122B-A10B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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